Himalaya

This study examines the patterns, trends, and causal factors of internal migration in Sikkim using Census of India data for 2001 and 2011 (D3 Migration Tables) together with the Multiple Indicator Survey (MIS) 2020–21. The results show that migration in Sikkim increased from 1,86,987 persons in 2001 to 2,47,049 in 2011, with the migration rate rising from 34.57% to 40.46%, above the national average of 38%. Intra-district migration is the dominant stream, growing from 48.37% to 51.00% of all migrants, while inter-district migration's share declined slightly despite an absolute increase. Interstate in-migration is concentrated from West Bengal, Bihar, and Uttar Pradesh, while out-migration flows toward more economically developed states such as Delhi, Maharashtra, and Karnataka. Marriage is the principal reason for female migration across all districts, while work and employment dominate male migration, with East District and its capital, Gangtok, emerging as the state's primary migration hub owing to its administrative, educational, and economic importance. A district-wise composite index of selected urban-development indicators confirms East District's pre-eminence, followed by South, North, and West Districts. The findings highlight the need for balanced regional development to reduce migration-driving disparities across Sikkim's districts.

Landslide risk in the Sikkim Himalaya is increasingly shaped by the building stock accumulating on steep, weathered slopes, yet quantitative information on the exposure of buildings to rainfall-triggered slope failure remains scarce at the settlement scale. This study couples a Random Forest (RF) landslide susceptibility model with a settlement-scale building inventory to quantify building exposure in South Sikkim, Indian Himalaya. Ten conditioning factors—slope, aspect, curvature, distance to rivers, distance to roads, distance to lineaments, Topographic Wetness Index (TWI), Land Use/Land Cover (LULC), Normalized Difference Vegetation Index (NDVI) and Stream Power Index (SPI)—were combined with a multi-source landslide inventory to train the classifier and produce a five-class susceptibility surface, which was then intersected with the building layer. The RF model attained an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.802, indicating good discriminative capability. High-susceptibility zones cover approximately 24% of the study area but contain 38% of the buildings, revealing a systematic over-representation of the built environment in the most hazardous terrain and a strong potential for cascading disruption of settlement services. The framework is data-parsimonious, reproducible, and directly transferable to comparable Himalayan settlements, providing an operational basis for hazard-informed regulation of new construction and for integrating exposure into landslide risk reduction in mountain regions.